Linear Precoder Design for Correlated Rician Fading Channel Under Imperfect CSI

In this paper,we have modeled a linear precoder for indoor multiuser multiple input multiple output(MU-MIMO)system with imperfect channel state information(CSI)at transmitter.The Rician channel is presumed to be mutually coupled and spatially,temporarily correlated.The imperfection with CSI is primarily due to the channel estimation error at receiver and feedback delay amidst the receiver and transmitter in CSI transmission.Along with,the insufficient spacing between the antenna at transmitter and receiver persuades mutual coupling(MC)among the array elements.In addition,the MIMO channel is presumed to be jointly correlated(Weichselberger correlation model).When we look back on the existing precoder design,it considered spatial correlation alone disregarding joint correlation of antenna array elements.With all above assumption,we have designed a linear precoder which minimizes mean squared error(MSE)subjected to total transmit power constraint for MUMIMO system.The simulation results proven that proposed precoder shows substantial enhancement in bit error rate(BER)performance in comparison with the existing technique.The mathematical analysis corroborates the simulation results.

Estimators of Linear Regression Model and Prediction under Some Assumptions Violation

The development of many estimators of parameters of linear regression model is traceable to non-validity of the assumptions under which the model is formulated, especially when applied to real life situation. This notwithstanding, regression analysis may aim at prediction. Consequently, this paper examines the performances of the Ordinary Least Square (OLS) estimator, Cochrane-Orcutt (COR) estimator, Maximum Likelihood (ML) estimator and the estimators based on Principal Component (PC) analysis in prediction of linear regression model under the joint violations of the assumption of non-stochastic regressors, independent regressors and error terms. With correlated stochastic normal variables as regressors and autocorrelated error terms, Monte-Carlo experiments were conducted and the study further identifies the best estimator that can be used for prediction purpose by adopting the goodness of fit statistics of the estimators. From the results, it is observed that the performances of COR at each level of correlation (multicollinearity) and that of ML, especially when the sample size is large, over the levels of autocorrelation have a convex-like pattern while that of OLS and PC are concave-like. Also, as the levels of multicollinearity increase, the estimators, except the PC estimators when multicollinearity is negative, rapidly perform better over the levels autocorrelation. The COR and ML estimators are generally best for prediction in the presence of multicollinearity and autocorrelated error terms. However, at low levels of autocorrelation, the OLS estimator is either best or competes consistently with the best estimator, while the PC estimator is either best or competes with the best when multicollinearity level is high(λ>0.8 or λ-0.49).

Uniform Convergence Rate of Estimators of Autocovariances in Partly Linear Regression Models with Correlated Errors

Consider the partly linear regression model , where y i ’s are responses, are known and nonrandom design points, is a compact set in the real line , β = (β 1, ··· , β p )' is an unknown parameter vector, g(·) is an unknown function and {ε i } is a linear process, i.e., , where e j are i.i.d. random variables with zero mean and variance . Drawing upon B-spline estimation of g(·) and least squares estimation of β, we construct estimators of the autocovariances of {ε i }. The uniform strong convergence rate of these estimators to their true values is then established. These results not only are a compensation for those of [23], but also have some application in modeling error structure. When the errors {ε i } are an ARMA process, our result can be used to develop a consistent procedure for determining the order of the ARMA process and identifying the non-zero coeffcients of the process. Moreover, our result can be used to construct the asymptotically effcient estimators for parameters in the ARMA error process.

MODEL CHECKING FOR GENERAL LINEAR ERROR-IN-COVARIABLES MODEL WITH VALIDATION DATA

In this paper, model checking problem is considered for general linear model when covariables are measured with error and an independent validation data set is available, Without assuming any error model structure between the true variable and the surrogate variable, the author first apply nonparametric method to model the relationship between the true variable and the surrogate variable with the help of the validation sample. Then the author construct a score-type test statistic through model adjustment. The large sample behaviors of the score-type test statistic are investigated. It is shown that the test is consistent and can detect the alternative hypothesis close to the null hypothesis at the rate n^-r with 0 ≤ r ≤1/2. Simulation results indicate that the proposed method works well. Key words General linear model, measurement error, model checking, validation data.

TESTING SERIAL CORRELATION IN SEMIPARAMETRIC VARYING COEFFICIENT PARTIALLY LINEAR ERRORS-IN-VARIABLES MODEL

The authors propose a V_(N,p) test statistic for testing finite-order serial correlation in asemiparametric varying coefficient partially linear errors-in-variables model.The test statistic is shownto have asymptotic normal distribution under the null hypothesis of no serial correlation.Some MonteCarlo experiments are conducted to examine the finite sample performance of the proposed V_(N,p) teststatistic.Simulation results confirm that the proposed test performs satisfactorily in estimated sizeand power.

Development cost prediction of general aviation aircraft projects with parametric modeling

The study of the development cost of general aviation aircraft is limited by small samples with many cost-driven factors. This paper investigates a parametric modeling method for prediction of the development cost of general aviation aircraft. The proposed technique depends on some principal components, acquired by utilizing P value analysis and gray correlation analysis. According to these principal components, the corresponding linear regression and BP neural network models are established respectively. The feasibility and accuracy of the P value analysis are verified by comparing results of model fitting and prediction. A sensitivity analysis related to model precision and suitability is discussed in detail. Results obtained in this study show that the proposed method not only has a certain degree of versatility, but also provides a preliminary prediction of the development cost of general aviation aircraft.

Comparison of MINQUE and Simple Estimate of the Error Variance in the General Linear Models

Abstract Comparison is made between the MINQUE and simple estimate of the error variance in the normal linear model under the mean square errors criterion, where the model matrix need not have full rank and the dispersion matrix can be singular. Our results show that any one of both estimates cannot be always superior to the other. Some sufficient criteria for any one of them to be better than the other are established. Some interesting relations between these two estimates are also given.

无缝线性回归与预测模型

建立回归模型常采用最小二乘方法并忽略自变量观测误差。尽管同时顾及自变量和因变量观测误差的总体最小二乘方法近年来得到了广泛研究,但在模型预测时,依然忽略了待预测自变量的观测误差。对此,本文提出了一种严格考虑所有变量观测误差的无缝线性回归和预测模型,该模型将回归模型的建立和因变量预测联合处理,在建立回归模型过程中对待预测自变量的观测误差进行估计并修正,从而提高了模型预测效果。理论证明,现有的几种线性回归模型都是无缝线性回归和预测模型的特例。试验结果表明,无缝线性回归和预测模型的预测效果优于现有的几种模型,尤其在变量观测误差相关性较大时,无缝模型对预测效果的改善更为显著。

具有一般协方差结构线性模型的局部影响评价

该文讨论了具有一般协方差结构线性模型的局部影响分析问题．通过对广义Cook统计量中M／c的适当选取,文章给出了一种对扰动的参数变换具有不变性质的局部影响度量,在具协方差扰动模式下,该文给出了回归系数和方差系数估计、最佳线性预测的局部影响诊断统计量．该结果与数据删除法进行了比较,并通过实例进行了分析和说明．

单向随机模型误差方差带约束的一种齐性检测

利用多元正态总体的复相关系数检验 ,给出了单向分类随机效应模型yij=μj+αi+εij具有线性约束I′ΛH =0的误差方差的一种齐性检测方法 .即检验H0 :σ21=…σ2 n,其中 ,Λ =diag(σ21,σ22 ,… ,σ2 n) ,R(Hm×t) =t,μ为常量 ,αi～N(0 ,σ20 ) ,εij～N(0 ,σ2 j) ,i=1,2 ,… ,n ;j=1,2 ,… ,m为随机效应 .各αi,εj 独立 ,I′ =(1,1,…… ,1) ,检验统计量为F =R21-R2 ·n -m +tm -t- 1～F(m -t- 1,n -m +t) ,拒绝域为W{F >Fα(m -t- 1,n -m +t) } .

相关误差的变系数模型的估计

在误差相关的情况下,使用局部线性方法给出变系数模型的估计,并讨论估计的渐近正态性。

网络RTK网外定位精度分析

现有网络RTK在网内的定位精度已经得到充分认证,能够达到厘米级,但网外的定位精度缺乏有效验证,对流层、电离层等误差的空间相关性限制了网外定位的作业距离。对网络RTK网外定位精度进行了详细分析,采用线性模型外推空间相关误差,用虚拟参考站技术搭建网络RTK,在网外不同位置检验定位精度。结果表明:定位精度随着用户离基站网距离的增加而降低,在参考站网络模糊度固定的情况下,用户位于网外20km范围内时,网络RTK基本能提供厘米级的定位精度,超过30km时,定位精度明显下降,且波动较大。

Geospatial Mapping of Soil Nitrate-Nitrogen Distribution Under a Mixed-Land Use System

Mapping the spatial distribution of soil nitrate-nitrogen （NO3=N） is important to guide nitrogen application as well as to assess environmental risk of NO3-N leaching into the groundwater. We employed univariate and hybrid geostatistical methods to map the spatial distribution of soil NO3-N across a landscape in northeast Florida. Soil samples were collected from four depth increments （0-30, 30-60, 60-120 and 120-180 cm） from 147 sampling locations identified using a stratified random and nested sampling design based on soil, land use and elevation strata. Soil NO3-N distributions in the top two layers were spatially autocorrelated and mapped using lognormal kriging. Environmental correlation models for NO3-N prediction were derived using linear and non-linear regression methods, and employed to develop NO3-N trend maps. Land use and its related variables derived from satellite imagery were identified as important variables to predict NO3-N using environmental correlation models. While lognormal kriging produced smoothly varying maps, trend maps derived from environmental correlation models generated spatially heterogeneous maps. Trend maps were combined with ordinary kriging predictions of trend model residuals to develop regression kriging prediction maps, which gave the best NO3-N predictions. As land use and remotely sensed data are readily available and have much finer spatial resolution compared to field sampled soils, our findings suggested the efficacy of environmental correlation models based on land use and remotely sensed data for landscape scale mapping of soil NO3-N. The methodologies implemented are transferable for mapping of soil NO3-N in other landscapes.

基于正交设计和BP神经网络模型的复鞣填充工艺优化

神经网络模型能够捕捉复杂系统中变量之间可能存在的非线性关系,常用于数据统计和预测中。使用丙烯酸树脂、栲胶、角蛋白/氨基树脂复合材料、合成鞣剂对蓝湿革进行复鞣填充,并利用正交设计和BP神经网络模型分析不同因素对坯革性能的影响。结果表明,以柔软度和撕裂力为评价指标,当中间层节点数为8和6时,神经网络模型可对复鞣填充进行高效拟合,均方误差MSE分别为0.113和0.279,线性拟合相关系数R^(2)分别为0.979和0.955。与正交设计相比,神经网络模型可更好地分析和预测坯革的性能,偏差<5%。

Jackknifed Liu Estimator in Linear Regression Models

In this paper, we introduce a generalized Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic errors. Therefore, we extend the Liu estimator. Under the mean square error（MSE）, the jackknifed estimator is superior to the Liu estimator and the jackknifed ridge estimator. We also give a method to select the biasing parameter for d. Furthermore, a numerical example is given to illustvate these theoretical results.

Testing for Error Correlation in Semi-Functional Linear Models

Existing methods for analyzing semi-functional linear models usually assumed that random errors are not serially correlated or serially correlated with the known order.However,in some applications,these assumptions on random errors may be unreasonable or questionable.To this end,this paper aims at testing error correlation in a semi-functional linear model(SFLM).Based on the empirical likelihood approach,the authors construct an empirical likelihood ratio statistic to test the serial correlation of random errors and identify the order of autocorrelation if the serial correlation holds.The proposed test statistic does not need to estimate the variance as it is data adaptive and possesses the nonparametric version of Wilks'theorem.Simulation studies are conducted to investigate the performance of the proposed test procedure.Two real examples are illustrated by the proposed test method.

Huber损失下线性模型的序列相关检验

金融数据分析中经常发现数据具有厚尾或非对称特征,这给模型相关性检验带来了许多困扰。针对厚尾和非对称分布的序列相关性检验问题,结合秩相关系数和Huber损失,提出了HubT、CCT检验统计量,并在原假设下得到了检验统计量的渐近分布。通过数值模拟说明新构建的统计量能在厚尾和非对称分布的序列中有良好的表现。

多雷达测量中的航迹位置相对熵

引入信息论中相对熵的概念,建立了基于位置信息的整体航迹相似性度量指标,并提出基于加权直线航迹线参数迭代估计模型的航迹位置相对熵计算方法;介绍了航迹位置相对熵在雷达相对系统误差估值有效性评价中的应用,在航迹关联应用试验中也取得了良好效果。

Generalized Ridge and Principal Correlation Estimator of the Regression Parameters and Its Optimality

In this paper,we propose a new biased estimator of the regression parameters,the generalized ridge and principal correlation estimator.We present its some properties and prove that it is superior to LSE(least squares estimator),principal correlation estimator,ridge and principal correlation estimator under MSE(mean squares error) and PMC(Pitman closeness) criterion,respectively.